From-scratch name disambiguation is an essential task for establishing a
reliable foundation for academic platforms. It involves partitioning documents
authored by identically named individuals into groups representing distinct
real-life experts. Canonically, the process is divided into two decoupled
tasks: locally estimating the pairwise similarities between documents followed
by globally grouping these documents into appropriate clusters. However, such a
decoupled approach often inhibits optimal information exchange between these
intertwined tasks. Therefore, we present BOND, which bootstraps the local and
global informative signals to promote each other in an end-to-end regime.
Specifically, BOND harnesses local pairwise similarities to drive global
clustering, subsequently generating pseudo-clustering labels. These global
signals further refine local pairwise characterizations. The experimental
results establish BOND's superiority, outperforming other advanced baselines by
a substantial margin. Moreover, an enhanced version, BOND+, incorporating
ensemble and post-match techniques, rivals the top methods in the WhoIsWho
competition.

借助本地和全局信息交换推动全球聚类，将具有相同姓名的作者独立为不同的真实专家群体，提出了一种名为 BOND 的从零开始姓名消歧方法，通过驱动全局聚类来生成伪聚类标签，并进一步细化本地成对特征，实验证明 BOND 优于其他相对先进的基线算法，而增强版 BOND + 在 WhoIsWho 竞赛中表现不逊于其他顶级方法。